2015
DOI: 10.1111/mec.13441
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Selection and sex‐biased dispersal in a coastal shark: the influence of philopatry on adaptive variation

Abstract: Sex-biased dispersal is expected to homogenize nuclear genetic variation relative to variation in genetic material inherited through the philopatric sex. When site fidelity occurs across a heterogeneous environment, local selective regimes may alter this pattern. We assessed spatial patterns of variation in nuclear-encoded, single nucleotide polymorphisms (SNPs) and sequences of the mitochondrial control region in bonnethead sharks (Sphyrna tiburo), a species thought to exhibit female philopatry, collected fro… Show more

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Cited by 99 publications
(119 citation statements)
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“…The same isolation of western North Atlantic and western South Atlantic populations at mitochondrial markers also occurred in large coastal shark species such as Sphyrna lewini (Griffith & Smith, 1834); (Chapman, Pinhal, & Shivji, ), Carcharhinus leucas (Müller & Henle, 1839) (Karl et al, ), G. cirratum (Karl et al, ), Negaprion brevirostris (Poey, 1868) (Ashe et al, ), and the semi‐oceanic C. falciformis (Domingues et al, ), but not in epipelagic, oceanic shark species such as Pseudocarcharias kamoharai (Matsubara, 1937) and Carcharhinus longimanus (Poey, 1861) (Camargo et al, ; Ferrette et al, ). Individual based mtCR analyses support an isolation‐by‐distance pattern rather than the complete isolation of populations in night sharks, similar to findings for other shark species such as Carcharhinus melanopterus (Quoy & Gaimard, 1824) (Vignaud, Clua, Mourier, Maynard, & Planes, ), Sphyrna tiburo (Linnaeus, 1758) (Portnoy et al, ), and N. brevirostris (Ashe et al, ). A mito‐nuclear discordance was also found for night shark along the western Atlantic Ocean: individual‐based analysis (DAPC and structure ), based on nine microsatellite loci, did not reveal the structure observed in mitochondrial markers.…”
Section: Discussionsupporting
confidence: 73%
“…The same isolation of western North Atlantic and western South Atlantic populations at mitochondrial markers also occurred in large coastal shark species such as Sphyrna lewini (Griffith & Smith, 1834); (Chapman, Pinhal, & Shivji, ), Carcharhinus leucas (Müller & Henle, 1839) (Karl et al, ), G. cirratum (Karl et al, ), Negaprion brevirostris (Poey, 1868) (Ashe et al, ), and the semi‐oceanic C. falciformis (Domingues et al, ), but not in epipelagic, oceanic shark species such as Pseudocarcharias kamoharai (Matsubara, 1937) and Carcharhinus longimanus (Poey, 1861) (Camargo et al, ; Ferrette et al, ). Individual based mtCR analyses support an isolation‐by‐distance pattern rather than the complete isolation of populations in night sharks, similar to findings for other shark species such as Carcharhinus melanopterus (Quoy & Gaimard, 1824) (Vignaud, Clua, Mourier, Maynard, & Planes, ), Sphyrna tiburo (Linnaeus, 1758) (Portnoy et al, ), and N. brevirostris (Ashe et al, ). A mito‐nuclear discordance was also found for night shark along the western Atlantic Ocean: individual‐based analysis (DAPC and structure ), based on nine microsatellite loci, did not reveal the structure observed in mitochondrial markers.…”
Section: Discussionsupporting
confidence: 73%
“…These “genomic” methods have frequently demonstrated a pattern whereby small numbers of loci (presumably under directional selection) show elevated divergence relative to the genomic mean in marine fishes (Portnoy et al. ; Hollenbeck et al. ; Anderson et al.…”
Section: Discussionmentioning
confidence: 99%
“…A simple visualization of expected and observed frequencies of homozygote genotypes across single nucleotide polymorphisms (SNPs) can be effective in identifying data problems (Figure 1). A simple model for estimating the heterozygote miscall (dropout) rate was applied to 12 publicly available RAD‐seq datasets (Fernández et al., 2016; Hecht, Matala, Hess, & Narum, 2015; Laporte et al., 2016; Larson et al., 2014; Le Moan, Gagnaire, & Bonhomme, 2016; Portnoy et al., 2015; Prince et al., 2017; Puritz, Gold, & Portnoy, 2016; Ravinet et al., 2016; Swaegers et al., 2015). While a few had low genotyping error rates (<5%), in others, allelic dropout, low read depth, PCR duplicates, erroneous assembly, and/or poor filtering resulted in much higher estimated error rates, with between 5% and 72% of heterozygotes apparently being miscalled as homozygotes.…”
Section: Genotyping Error and Improving Data Qualitymentioning
confidence: 99%